161 citations · 628 across the 32 of their papers we have counts for
34 papers
STDec: Spatio-Temporal Stability Guided Decoding for dLLMs
Yuzhe Chen, Jiale Cao, Xuyang Liu +3
Diffusion Large Language Models (dLLMs) have achieved rapid progress, viewed as a promising alternative to the autoregressive paradigm. However, most dLLM decoders still adopt a gl…
SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration
Ronghua Xu, Jin Xie, Jing Nie +2
Spiking Neural Networks (SNNs), characterized by discrete binary activations, offer high computational efficiency and low energy consumption, making them well-suited for computatio…
SSLFusion: Scale & Space Aligned Latent Fusion Model for Multimodal 3D Object Detection
Bonan Ding, Jin Xie, Jing Nie +1
Multimodal 3D object detection based on deep neural networks has indeed made significant progress. However, it still faces challenges due to the misalignment of scale and spatial i…
CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks, leading to the research to adapt CLIP for pixel-level…
iSeg: An Iterative Refinement-based Framework for Training-free Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Stable diffusion has demonstrated strong image synthesis ability to given text descriptions, suggesting it to contain strong semantic clue for grouping objects. The researchers hav…
Multi-Granularity Language-Guided Training for Multi-Object Tracking
Yuhao Li, Jiale Cao, Muzammal Naseer +4
Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same…
Vision Foundation Model Driven Foreground-Aware Pseudo-LiDAR Generation for Monocular 3D Object Detection
Bonan Ding, Jin Xie, Jing Nie +2
Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations that can be processed by LiDAR-base…
Implicit and Explicit Language Guidance for Diffusion-based Visual Perception
Hefeng Wang, Jiale Cao, Jin Xie +2
Text-to-image diffusion models have shown powerful ability on conditional image synthesis. With large-scale vision-language pre-training, diffusion models are able to generate high…
CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance Segmentation
Wenqi Zhu, Jiale Cao, Jin Xie +2
Open-vocabulary video instance segmentation strives to segment and track instances belonging to an open set of categories in a videos. The vision-language model Contrastive Languag…
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation
Bin Xie, Jiale Cao, Jin Xie +2
Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-train…
A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos
Chao Qin, Jiale Cao, Huazhu Fu +2
Detecting breast lesion in videos is crucial for computer-aided diagnosis. Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of…
DFormer: Diffusion-guided Transformer for Universal Image Segmentation
Hefeng Wang, Jiale Cao, Rao Muhammad Anwer +3
This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diff…
Transformer-based stereo-aware 3D object detection from binocular images
Hanqing Sun, Yanwei Pang, Jiale Cao +2
Transformers have shown promising progress in various visual object detection tasks, including monocular 2D/3D detection and surround-view 3D detection. More importantly, the atten…
LEAPS: End-to-End One-Step Person Search With Learnable Proposals
Zhiqiang Dong, Jiale Cao, Rao Muhammad Anwer +3
We propose an end-to-end one-step person search approach with learnable proposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search…
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search
Jiabei Wang, Yanwei Pang, Jiale Cao +3
Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only person bounding-box annotations. Recently, the idea of contrastiv…
Multi-scale Feature Aggregation for Crowd Counting
Xiaoheng Jiang, Xinyi Wu, Hisham Cholakkal +5
Convolutional Neural Network (CNN) based crowd counting methods have achieved promising results in the past few years. However, the scale variation problem is still a huge challeng…
3D Vision with Transformers: A Survey
Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan +4
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replace…
TransFiner: A Full-Scale Refinement Approach for Multiple Object Tracking
Bin Sun
Multiple object tracking (MOT) is the task containing detection and association. Plenty of trackers have achieved competitive performance. Unfortunately, for the lack of informativ…
PSTR: End-to-End One-Step Person Search With Transformers
Jiale Cao, Yanwei Pang, Rao Muhammad Anwer +4
We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR com…
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
Omkar Thawakar, Sanath Narayan, Jiale Cao +6
State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features dur…
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection
Aqi Gao, Yanwei Pang, Jing Nie +2
Fast stereo based 3D object detectors have made great progress recently. However, they lag far behind high-precision stereo based methods in accuracy. We argue that the main reason…
Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection
Aqi Gao, Jiale Cao, Yanwei Pang
Pseudo-LiDAR based 3D object detectors have gained popularity due to their high accuracy. However, these methods need dense depth supervision and suffer from inferior speed. To sol…
Track to Detect and Segment: An Online Multi-Object Tracker
Jialian Wu, Jiale Cao, Liangchen Song +3
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and t…
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
Tiancai Wang, Tong Yang, Jiale Cao +1
Object detectors usually achieve promising results with the supervision of complete instance annotations. However, their performance is far from satisfactory with sparse instance a…